Embodiments of the present invention relate generally to control systems, and more particularly to model predictive control employing novel techniques for the sharing of variable trajectories between two or more controllers.
Various control system designs currently exist for a variety of industrial applications. In general, feedback control systems provide for the sensing one or more detectable parameters of a process, and drive a controlled variable to a desired level on the bases of one or more parameters, which may be sensed or provided by an operator (e.g., user-specified set point), for instance. The basis for such control system designs may include parametric models, neural network models, linear and non-linear models, to name only a few.
In model predictive control (MPC) systems, one or more MPC controllers may utilize a dynamic multivariable predictive model representing relationships among multiple process variables, including both manipulated variables and control variables. As will be appreciated by those skilled in the art, control variables (CV's) are those variables that a controller tries to bring to some objective (e.g., to a target value, maximum, etc.). Manipulated variables (MV's) may be referred to as those which the process has control authority over, and which are moved or manipulated by a controller in order to achieve the targets or goals indicated by the CV's. Control of the MV's may, however, be limited by operating constraints imposed against a particular controller, as discussed below.
MPC controllers may utilize dynamic multivariable predictive models for defining an input/output relationship for a process that not only reflects how much an output changes in response to an input change, but may also reflect the rate (e.g., time-dependent function) at which an output will change based on one or more input variable changes. The dynamic predictive models may also be based upon knowledge of any operating constraints, including both controllable (e.g., those that the process has discretion to change) and external constraints (e.g., those relating to safety, environmental, physical, legal or other system limitations) with regard to particular process variables.
An MPC controller may derive or predict “target profiles” or anticipated trajectories representing desired future values or set points for particular process variables over a period of time. The trajectories may be predicted or derived based on prior and present knowledge of certain process variables as well as the input/output relationships and/or constraints associated with such variables, as discussed above. Accordingly, process control may be implemented to achieve one or more control objectives based on these predicted variable trajectories. For instance, based on a desired trajectory, an MPC controller may implement control actions in order to “move” or adjust a process variable towards a particular set point or target defined by the predicted trajectory.
A particular problem with existing control systems arises when multiple controllers in a control system are designed such that their control outputs are based on one or more common process variables. Because the constraints imposed on each controller and other process variables received by each controller may vary, the variable trajectories predicted by each of the multiple controllers for the common process variable may differ. Thus, effective control of the common variable based on the differing trajectories may be difficult. Accordingly, there exists a need for a technique to resolve differing trajectory forecasts among multiple controllers relying on common variables to implement control actions in a process control system.